Optical transillumination and reflectance spectroscopy to quantify disease risk

ABSTRACT

The present invention uses spectroscopic tissue volume measurements using non-ionizing radiation to detect pre-disease transformations in the tissue, which increase the risk for this disease in mammals. The method comprises illuminating a volume of selected tissue of a mammal with light having wavelengths covering a pre-selected spectral range, detecting light transmitted through, or reflected from, the volume of selected tissue, and obtaining a spectrum of the detected light. The spectrum of detected light is then represented by one or more basis spectral components, an error term, and an associated scalar coefficient for each of the basis spectral components. The associated scalar coefficient is calculated by minimizing the error term. The associated scalar coefficient of the each of the basis spectral components is correlated with a pre-selected property of the selected tissue known to be indicative of susceptibility of the tissue for the pre-selected disease to obtain the susceptibility for the mammal to developing the pre-selected disease.

CROSS REFERENCE TO RELATED UNITED STATES PATENT APPLICATION

This patent application is a National Phase application claiming thebenefit of PCT/CA02/01771 filed on Nov. 20, 2002; which further claimspriority benefit of U.S. provisional patent application, Ser. No.60/331,633, filed on Nov. 20, 2001, entitled OPTICAL TRANSILLUMINATIONSPECTROSCOPY TO QUANTIFY DISEASE RISK.

FIELD OF THE INVENTION

The present invention relates to the use of opticaltransillumination/reflectance spectroscopy as a diagnostic fordetermination of the risk for diseases.

BACKGROUND OF THE INVENTION

As early cancer or disease detection and diagnosis make further inroadsinto clinical practice, the high cost of current disease screeningtechniques redirected the focus of the investigators towards methodscapable of quantifying the risk towards these diseases as a practicalpre-screening tool. One particular application area is epidemiologicalscience and public health research into cancer prevention, so theinvention is applicable for a wide variety of chronic or slowlydeveloping diseases such as Alzheimer's, or Multiple Sclerosis. However,prevention required identification of the population at risk inassociation with an appropriate prevention or risk reductionintervention for example through the control of disease inducing agentsor lifestyle changes (exercise, diet etc.) An example riskquantification is the work related to assessing the breast cancer riskin the general population or subgroups thereof by Boyd, Yaffe et al.(1-3) which showed that X-ray density patterns are identified as havingone of the highest odds ratios towards the risk of breast cancer betweenthe low risk and the high risk groups. For the specific case of breastcancer, radiologically dense breast tissue on mammography indicates thepresence of stromal and epithelial tissue in the breast, the amount ofwhich is strongly related to risk of breast cancer, with increasingamount of radiologically dense tissue related to increased risk. Theability to assess the breast cancer risk enables new steps in cancerprevention, for example through lifestyle and dietary changes (4).

One of the major disadvantages of the current standard for Breast CancerRisk assessment is the use of ionizing radiation. This results in thelate introduction of this diagnostic modality during the life of awoman, due to the inherent risk when using ionizing radiation in adiagnostic modality. Other good risk predictors are in general also onlyavailable once a woman has reached around 40 years of age, such ascancer incidence in first degree relatives (mother and sisters).However, the late onset in using these risk assessment modality willreduce the available time frame for any intervention aimed at reducingthe disease risk. Hence, there is a clear need for a non-ionizingmodality which can be employed in young patients, here in post pubertypre-menopausal women.

Non-ionizing radiation was employed in various optical mammographyapproaches, usually in attempts to image the breast, and to detectbreast lesions (5,6) commonly using frequency domain technologies atonly a few specialized wavelengths, or as spectroscopic approaches forthe determining the tissue optical properties of normal versus malignantbreast tissue (7). These spectroscopic applications, including anarticle by Egan and Dolen (8) are rather intended for determination ofthe probability for the presence of cancer, but do not address theconcept of risk assessment, e.g. as a pre-screening tool.

U.S. Pat. No. 6,121,775 is directed to an MRI imaging method andapparatus and provides a physical interrogation methods related todetecting small changes in tissue.

U.S. Pat. No. 5,079,698 is directed to a transillumination method andapparatus for the diagnosis of breast tumors and other breast lesions bynormalization of an electronic image of the breast. U.S. Pat. No.6,002,958 is directed to a method and apparatus for diagnostics ofinternal organs. This patent teaches the use of NIR radiation in the0.6-1.5 um wavelength range and adds ultrasound to the analysis tools.These two patents specifically create images of the organ

U.S. Pat. No. 6,095,982 discloses a spectroscopic method and apparatusfor optically detecting abnormal mammalian epithelial tissue' coversonly Raman and fluorescence methods. U.S. Pat. No. 6,069,689 disclosesan apparatus and methods relating to optical systems for diagnosis ofskin diseases while very generally written and addressing, reflectance,fluorescence and Raman, using a plurality of light emitting diodes.While some changes in the tissue (skin) are mentioned the idea of riskassessment is not included in this or any other patent related to theuse of non-ionizing radiation.

SUMMARY OF THE INVENTION

The present invention uses spectroscopic tissue volume measurementsusing non-ionizing radiation to detect pre-disease transformations inthe tissue, which increase the individuals risk for this disease.

In one aspect of the present invention there is provided a method forassessing susceptibility for developing a pre-selected disease in amammal, comprising:

a) illuminating a volume of selected tissue of a mammal with lighthaving wavelengths covering a pre-selected spectral range;

b) detecting light transmitted through, or reflected from, said volumeof selected tissue, and obtaining a spectrum of said detected light;

c) representing the spectrum of detected light by a set of basisspectral components, an error term, and an associated scalar coefficientfor each basis spectral component in said set, the set of basis spectralcomponents including at least one basis spectral component, theassociated scalar coefficient for each basis spectral component beingcalculated by minimizing the error term; and

d) correlating the associated scalar coefficient for each spectralcomponent with a pre-selected property of the selected tissue known tobe indicative of susceptibility of the tissue for the pre-selecteddisease to obtain the susceptibility for the mammal to developing thepre selected disease.

In another aspect of the invention there is provided an apparatus forassessing susceptibility for developing a pre-selected disease in amammal, comprising:

a) holder means for holding and immobilizing an anatomical part of amammal containing tissue to be optically probed;

b) light source means for illuminating a volume of selected tissue of amammal with light having wavelengths covering a pre-selected spectralrange;

c) detection means for detecting light transmitted through, or reflectedfrom, said volume of selected tissue;

d) computer control means connected to said detection means forproducing a spectrum of said detected light from an output of saiddetection mean, the computer control means including processing meansfor representing the spectrum of detected light by a set of basisspectral components, an error term, and an associated scalar coefficientfor each spectral component in said set, the set of basis spectralcomponents including at least one basis spectral component, theassociated scalar coefficient for each basis spectral component beingcalculated by minimizing the error term, the processing means includesmeans for correlating the associated scalar coefficient for each basisspectral component with a pre-selected property of the selected tissueknown to be indicative of susceptibility of the tissue for thepre-selected disease to obtain the susceptibility for the mammal todeveloping the pre-selected disease, the computer control meansincluding display means for displaying the susceptibility.

BRIEF DESCRIPTION OF THE DRAWINGS

The method and apparatus constructed in accordance with the presentinvention will now be described, by way of example only, reference beinghad to the accompanying drawings, in which:

FIG. 1 shows the block diagram of one possible embodiment of anapparatus produced in accordance with the present invention;

FIG. 2 show a reconstruction of an experimentally obtained opticaltransillumination spectrum by a linear combination of either only thefirst two or four principle components.

FIG. 3 shows an example of a typical set of measurements comprised ofeight spectra from a volunteer representing the four quadrant of thebreast on either side of the bilateral organ;

FIG. 4 shows the correlation of the t₁ and t₂ scores from the spectrathat were repeatedly measured resulting in a regression slope andcorrelation coefficient close to unity;

FIG. 5 showing the first 4 components obtained from the PCA followingdata pre-processing which included thickness and transfer functioncorrection, note that component 2 show inverse absorption for the lipidand water peaks respectively;

FIG. 6 shows the resulting weights or loading factors t₁ vs. t₂ for thefirst 2 principle components for thickness and transfer functioncorrection as pre-processing option, square and rhombus symbolsrepresent high and low breast density subjects, respectively;

FIG. 7 shows the first four components obtained from the PCA followingdata pre-processing which includes only transfer function correction;

FIG. 8 shows the resulting weights or loading factors t₁ vs. t₂ for thefirst two principle components using transfer function correction asonly pre-processing option, square and rhombus symbols represent highand low breast density subjects, respectively;

FIG. 9 shows the first 4 components obtained from the PCA following datapre-processing which includes thickness and transfer function correctionfollowed by autoscaling of the data;

FIG. 10 shows the resulting weights or loading factors t₁ vs. t₂ for thefirst 2 principle components using thickness and transfer functioncorrection followed by autoscaling of the data, square and rhombussymbols represent high and low breast density subjects, respectively;

FIG. 11 assigns physical meaning to the four quadrants in the t₁ vs. t₂plots, shown here for thickness and transfer function corrected andautoscaled data

FIG. 12 shows an identification of spectra form low and high densitybreast-tissue within a 3D plot of t₁ vs. t₂ and t₃ using thickness andtransfer function correction as pre-processing options;

FIG. 13 shows the predicted % parenchymal tissue density based on PLSanalysis of the optical transillumination spectra versus percent densityaccording computer assisted analysis of the mammograms, solid symbolsrefer to the training set and open symbols to the validation set;

FIG. 14 shows spectrally resolved transillumination and reflectancemeasurements are obtained by the used of one of more appropriate VIS/NIRlight sources and opto-electronic detector systems measuring the surfaceproximal volume layer and the total volume separately; and

FIG. 15 shows an example of a three wavelength frequency domain systemto determine the differential path-length factor of photons traversingtissue.

DETAILED DESCRIPTION OF THE INVENTION

Referring to FIG. 1 shows an apparatus 10 used in the present inventioncomprises a light source 12, waveband selection devices, deliveryoptical fibre 14, tissue support 16, collection optical fibre bundle 18,wavelength dispersing element, opto-electronic detector 20 and CPU 22.Spectra can be corrected for the wavelength dependent transfer functionof the instrument and the physical interoptode distance. It will beunderstood that this is not required as it can also be convoluted intothe numerical correlation analysis. Additionally, the optical fibre 14,the tissue support and the optical fibre bundle is optional. Thewavelength selective device can be either prior to or post theinterrogated tissue.

For use in studies related to breast tissue, the light source 12 was a12 Watt halogen lamp (Welch Allyn) with stabilized power supply servingas a broadband light source. The ultraviolet to green and mid infraredradiation were eliminated using a cut-off filter (at 500 nm) and a heatfilter, respectively. The remaining light was coupled into a 5 mmdiameter liquid light guide, which served as the source fiber in contactwith the skin on the top of the breast. A total power of 0.25 Watt,covering the 550-1300 nm bandwidth, was delivered to the skin surface.The holder of the source fiber and the plate in which the detector fiberwas embedded were made of black plastic to absorb reemitted photons. Thesource fiber was in firm contact with the tissue and slightly compressedit by 5 mm. The source and detector fibers (optodes) were heldcoaxially, pointing towards each other, via a custom-made calliperattached to the resting platform. The calliper allowed the measurementof the inter-optode distance (cm) and hence, the physical thickness ofthe interrogated tissue. Transmitted light was collected via acustom-made 7 mm diameter fiber bundle (P&P Optical Kitchener, Ontario,Canada). Wavelength resolved detection in the visible-near infrared wasachieved using a holographic transillumination grating (15.7 rules/mmblazed at 850 nm) (Kaiser, Calif., USA) and a 2D, cryogenically-cooledsilicon CCD (Photometrics, New Jersey, USA) at a spectral resolution ofless than 3 nm between 625 nm and 1060 nm. This spectral resolution wasachieved by positioning a 0.5 mm slit between the distal end of thecollection fiber and the spectral grating. The peak quantum efficiency(>0.8) of the detector was at 780 nm, falling to 0.2 at 1100 nm. Byimaging the entrance slit of the spectrograph onto the 2D CCD, 50 rowsof pixels were effectively exposed to detected light, thus increasingthe dynamic range of the electronic detection by up to 50 times. Tominimized background noise, cryogenic cooling reduces the dark counts to˜0.06 electrons per hour. Further noise reduction was achieved usingexposure times of 2-3 seconds and averaging up to five scans. Thesystem's dynamic range was >5 OD (optical densities) with asignal-to-noise ratio of >10⁴ at peak sensitivity and >10 across theremaining bandwidth of the system.

In order to collect the transmitted light emerging from the tissue, awavelength dispersing medium (in the preferred embodiment a grating) forthe various wavelength ranges use, if present, and an opto-electronicdetector, here a Si based (0.65 μm to 1.5 μm) photodiode array (PDAs)are used. The PDA's information is transferred directly or delayed tothe computer containing a numerical analysis program to quantify thedisease risk using a previously trained algorithm. The correlationbetween spectral data and disease risk is based on the spectralattenuation, through photon absorption and scattering, only. Theparticular disease risk assessment system comprises in its currentversion a continuous optical spectrum with wavelength in the 550 nm to1000 nm range.

Different embodiments of the apparatus may include an InGaAs based (0.9μm to 1.7 μm), or equivalent opto-electronic detector to cover also thelonger wavelength range which is transmitted through tissue, or cancomprise also only various wavelengths or wavelength bands within theVIS/NIR range, such as attainable with filters of direct emissiondevices with limited bandwidth such as laser, LED or similar.

In another embodiment of the apparatus there is included at least asingle

(whereas three is preferred) wavelength, frequency domaintransillumination system, spanning most of the steady state wavelengthrange for transillumination. In one non-limiting embodiment the threewavelengths may be 785, 808, 905 nm. The lasers in the frequency domainsystem need to be modulated between 10 to 400 MHz (215 MHz in thepresent system) to obtain a good phase shift resolution for 5 cmphysical pathlength between delivery and detection optode. The systemshould provide a phase shift resolution of better than 20. The lasersused here provide ˜200 mW average power at the distal end of thedelivery fibers in contact with the patient. Intensity modulation isachieved by modulating the driving current of the lasers via a biased-T,a method to recapture the transmitted light and guide it to the PMT withfast time response, a preamplifier filtered to the appropriatemodulation frequency, a lock-in detection to quantify the demodulationand the phase shift compared to a standard either from the laser drivercurrent or a portion of the light prior to entering the tissue, and adata transfer system to a computer to translate the phase shift anddemodulation data into a differential path length factor and scatteringcoefficient. In frequency domain measurements various modi operandi canbe employed one being heterodyne detection technique, which can use alsovery lower power lasers. Other methods to quantify the opticalpathlength are described (10, 11) and include time resolved detection ortime resolved single photon detection.

The ability to quantify disease risk and hence the population members atrisk can be increased through additional measurements such as skinreflectance measurements to be able to subtract the skin contribution tothe optical transillumination spectrum, so clearly this information maynot be required. Depending on the part of the body being diagnosedadditional components such as anatomical supports may be included, forexample if the breasts are being studied, a support stand for thebreast, and a holder for the liquid light guide and optical fibres forlight delivery, and a random fibre bundle for light collection. Thelatter may be a trifurcated fibre bundle to deliver transmitted photonsto the PMT (frequency domain measurements), Si-based and InGaAs-basedPDAs respectively. Again these are to a large extent optional, forexample one can envision a cup like device which holds the sources anddetectors in direct contact with the tissue. A means to measure theangle and distance between the delivery and detection optodes equivalentto the physical tissue thickness or in the case of a reflectancemeasurement the interoptode distance as the latter will determine thetissue depth most likely interrogated by the photons. It is noted thatthe inventors have shown some correlation and predictive abilities withthickness correction of the data. Software to extract spectralabsorption and light scattering features to quantify the risk for aspecific disease is loaded onto the CPU. Software was trained using datafrom clinical studies providing the optical spectra and an independentmeasure of the risk as discussed hereinafter.

In order to correct for variations in the wavelength dependent signaltransfer function of the system, all transillumination spectra werereferenced to a daily collected transmission standard made of 1 cmthick, ultra-high density polyurethane (Gigahertz Optics, Munich,Germany). All measured spectra are thus given as wavelength dependent(rel OD). This referencing to a known standard yields a universalapplicable dataset and hence the subsequent developed mathematicalmodels correlating transillumination spectra with risk will also becomeuniversally valid, that is independent of the actual instrument used tocollect the data.

The spectral volume measurements can be augmented by adding extendedlong wavelength (comprising the NIR range transmitted through tissue,e.g. up to 1.7 μm) and frequency domain measurements. The formerproviding more information about vibrational bands of the biomoleculesin the interrogated volume. The latter enables by using a limited numberof wavelengths (3 or more), to determine the differential pathlengthfactors of the tissue (6). Combining this with the transilluminationspectra allows one to obtain absolute absorption spectra andsubsequently derivation of the contributions of various chromophoresresponsible for the absorption spectra measured providing additionalinformation for the identification of the population at risk and alsoinsight into the molecular changes associated with or resulting from thetissue transformation.

Analysis of Reflectance or Transillumination Optical Data for Assessmentof Disease Risk

The following describes four nonlimiting examples of mathematicalmodelling techniques capable of establishing a correlation between theoptical spectra and a particular outcome, in the present case risk ofdisease. This is not a complete or comprehensive list of all the methodsavailable which may be used and other methods such as hybrid linearanalysis (12) are available and persons skilled in this type of analysiscan identify other methods. These spectral analysis techniques have beenused extensively in chemometrics field to solve for the concentration ofa constituent of interest without knowing the spectra of allconstituents present in a chemical sample (13) or where multiplechemicals of interest have overlapping spectra, (14). These analysistechniques construct models that identify the variance within thespectral data set or, when trained with a known range of concentrations,can identify the variance that is relevant to the constituent(s) ofinterest. Through training the model derives component spectra thatresemble the constituent spectra within the sample, and can later beused to predict the concentrations of constituents from new samplespectra. However, these component spectra may not have direct physicalmeaning but can represent the spectral features of the pure constituentspectra.

In the case of breast cancer which is exemplified hereinafter, it hasbeen shown by Thompson and Tatman (9) that the tissue composition willchange slowly as the breast is undergoing sequential changes towardsdysplasia, carcinoma in situ and then invasive cancer, often involvingor resulting in concentration change of different optically (lightabsorption or fluorescence) active molecules and/or structures (lightscattering).

In the specific case of breast cancer the idea is based on the fact thatthe same structures and chemicals, that give rise to the x-rayattenuation, the current (gold) standard for breast cancer riskassessment, will also result in changes in the transilluminationspectra. These changes will be evident in the attenuation and/orscattering of the visible/NIR light due to different contributions ofthe optically active molecules. Correlation between the x-raymammography and the spectral transillumination information can beestablished and quantified by a variety of different numerical methods,among which are principal component analysis or linear discriminateanalysis when the breast density based on mammographic analysis isprovided as classification (nominal data) or using partial least squaresor principle component regression when the mammographic analysis isprovided as % dense tissue (interval data). Besides x-ray mammographicanalysis, other methods which may be used to obtain the parenchymalbreast tissue density may include ultrasound, computed tomography, conebeam computed tomography, electrical impedance spectroscopy and magneticresonance imaging.

Principal Component Analysis (PCA) and Linear Discriminant Analysis(LDA)

PCA is optimized for comparison of vectors with nominal data, in thiscase correlating spectra to the density classification, thereby makinguse of the complete spectral information while reducing the datasetsize. For this, PCA determines the amount of variance within the testgroup of spectra and uses it to iteratively reduce the dataset torepresentative spectra, called components. The spectral data arerepresented by a smaller number of vectors in a lower dimensional spacewhile still including the maximum amount of variance within the dataset. (15). This is accomplished by solving for the covariance orcorrelation matrix of the data matrix X (m×n) comprised of the datasetof all spectra obtained (n) and the spectral range monitored (m), suchthat:

$\begin{matrix}{{{cov}(X)} = \frac{X^{T}X}{n - 1}} & (1)\end{matrix}$PCA decomposes the data matrix X as the sum of the outer products of thevectors t_(i) and p_(i) and a residual matrix E:X=t ₁ p ^(T) ₁ +t ₂ p ^(T) ₂ +t ₃ p ^(T) ₃ + . . . +t _(i) p ^(T) _(i)+EorX=TP ^(T+E)  (2)Where the elements of the t_(i) (n×1) vectors are the scores thatcontain information on how the spectra relate to each other, and thep_(i) vectors (m×1) or components are the eigenvectors of the covariancematrix and show how the selected variance relates to each other. Thecomponents p_(i) are eigenvectors of the covariance matrix, so thatcov(X)p _(i)=λ_(i) p _(i)  (3)where λ_(i) is the eigenvalue associated with the eigenvector p_(i).Also for any X and t_(i) and p_(i) pair X p_(i)=t_(i) (i.e. the scorevector t_(i) is the linear combination or representation of the originalX defined by p_(i)). It is generally found that data can be described infewer components p_(i) than original variables (m) and that thecomponents can be combinations of variables that are useful descriptionsof particular constituents in the tissue. As stated above, the shape ofthe useful components will be a combination of spectral signatures ofchromophores that vary with tissue density. Scores (elements of t_(i))that differentiate between tissue densities identify useful components(p_(i)).

The scores (elements of t_(i)) can be graphically plotted against oneanother to show any potential clustering of spectra that are related. Inthis study PCA was calculated on the test set (⅔ of available spectra)and the same mathematical model, e.g. retaining the p_(i) was used topredict the scores t_(i) on the validation set (⅓ spectra).

Linear Discriminant Analysis (LDA) was used on the PCA scores to enhancethe differentiation between the two extreme nominal categories (low vshigh density). LDA finds a discriminant rule for defined groups withinthe dataset. It is based on drawing the boundary halfway between themeans on a pair wise basis (adjusted slightly if there is some priorprobability of group identity). It calculates the discriminant rule bycomputing optimizing factors (criterion _(j)) that are based on thecovariance matrix of the given groups. The targets that are used fortraining identify the density category group (16)criterion_(j) =inv(cov X _(j))·S _(b)  (4)Where j is the particular group and S_(b) is the between class variancedefined by

$\begin{matrix}{S_{b} = {\sum\limits_{j}\left( {x_{j} - \overset{\_}{x_{n}}} \right)^{2}}} & (5)\end{matrix}$In this study, the LDA algorithm was trained using the training datasetby leaving one spectrum out during each training run and then predictingit's tissue density group. The rest of the spectra from the validationset were then classified using the trained model. The LDA scores canalso be plotted in a similar cluster diagram as the PCA scores (16)Table 1 shows the key to differences between PCA and LDA.

Statistical significance for the PCA and LDA prediction was establishedusing measures similar to sensitivity and specificity values commonlyused to evaluate the validity of diagnostic tests. As transilluminationis currently envisioned as a method for identifying those women withhigh tissue density within the entire population, increased HDM ispreferable over increased LDM. Consequently, a high density measure(HDM) was used to assess the quality of the prediction model. HDM wasdefined as the ratio of those women that were predicted to have highdensity tissue with the optical transillumination method to those whowere categorized as having high tissue density by the Radiologist.Conversely, the low density measure (LDM) is a measure of the ability tocorrectly identify those spectra that do not have high density.

TABLE 1 Properties of Principal Component Analysis and LinearDiscriminant Analysis. PCA LDA Input data Spectral vector Requires PCAscores as input Result space size Undefined since # of Defined bytraining e.g. classifications PC's is undefined procedures Datareduction Vector->scalar Scalar->scalar Training or Density No Yesreadings needed Presentation of Relationship of Relationship of outputdata scalars by cluster scalars by categories analysis or clusteranalysis What variance Those captured in The variance between is usedprincipal components the defined groups Additional output Componentvectors Discriminant rule no information contain physical apparentphysical meaning meaning

FIG. 2 shows a reconstruction of an experimentally obtained spectrum byeither only two or four principle components.

Principal Component Regression and Partial Least Squares Analysis

PCR and PLS are analysis techniques are optimized for comparison ofvector inputs with interval target data. For this case the spectra arecompared to the percent density readings. Statistical significance wasestablished using the Pearson correlation coefficient as well as theslope and intercept of the regression line. Both PCR and PLS analysisrequire training and perform regression analysis on the dataset, but usedifferent types of variance identification to predict the targetvariable, in this study the percent density read from the mammogram, seeTable 2.

As a first attempt for solving for a concentration of a constituent,Inverse Least Squares analysis has been used for well behaved data,where spectral features are not overlapped and predictions of theconstituent is not effected by collinearity problems with otherconstituents in the sample. Here Y is defined as:Y=Xb  (6)Where b is the regression vector that is found by solving for thepseudoinverse of X. This cannot be used here since all the spectralfeatures contributing to the spectral shape and that lead to densityprediction are not defined so there could be collinearity problems.

Principle Component Regression calculates principal components in asimilar manner to Principal Component Analysis and then performsregression analysis of the scores matrix with the known targets ortissue densities from the training set. It does this by solving thefollowing equationY=(PT ^(T))b  (7)where b=P(T ^(T) T)⁻¹ T ^(T) Y  (8)which is similar for Inverse Least Squares analysis above in equation 7,except that X=PT^(T). The regression vector b is the least squaressolution for T, where Tare scores from the PCA and T is a (n×k) matrix,where n is the number of spectra and k is the number of factors used inthe model. Y is the (n×1) vector of targets, in this case percentdensity of the region of interest of the mammogram (17).

For Partial Least Squares Analysis, the basis vectors or latentvariables (equivalent to the principal components) are extracted usingthe related targets, such as percent density, by solving equation 7 andrelating b to the targets directly by solving:b=W(P ^(T) W)⁻¹(T ^(T) T)⁻¹ T ^(T) Y  (9)where W is a weighting vector that relates the target set (predictionvalues) with the variance in the dataset during the decomposition intofactors. This allows the PLS algorithm to solve for components that arecorrelated with the targets (Y) while describing a large amount of thevariation in X. (17) For a more detailed explanation please see KowalskiB R and Geladi P (1986), (17) Wise B M (2000), (15) and Haaland D M andThomas E V (1988). (14).

With both PCR and PLS only a single regression vector b is used insubsequent predictions of spectra. This b vector is selected bycalculating the predicted residual error sum of squares (PRESS) valuefor each b_(i) using the training set. The chosen b vector has thefewest number of iterations (lowest value of i) and the lowest PRESSvalue when the training set is used to establish the model.

TABLE 2 Properties of Principal Component Regression and Partial LeastSquares Analysis. PCR PLS Inputs Density (targets) Density (targets) andspectra for a and spectra for a training set training set Data reductionNo No Density readings For each spectrum For each spectrum in needed intraining set training set Presentation of Estimated percent Estimatedpercent output data density density What variance is used Those capturedin The variance that principal components matches the targets Additionaloutput info b vector b vector

In this study the PRESS values were calculated for both PCR and PLS forthe training sets, using the built-in cross-validation function inMatlab 6.0 PLS toolbox. The b vector with the lowest PRESS value wasselected. The training set was randomized and split into four sections,the respective algorithms were then trained by a Venetian blind methodwith four repeats. The subsequent b vectors were then averaged and usedto predict the tissue density for entire training set and subsequentlythe validation set.

The method disclosed herein will now be described with a nonlimitingexample of study in breast tissue.

EXAMPLE Breast Tissue Transillumination Study

Transillumination spectroscopy measurements were taken in a dark roomwith the volunteer seated comfortably, and the breast resting on ahorizontal support (FIG. 1). Total data acquisition time wasapproximately 15 minutes allowing measurements at all 8 positions andnecessary re-positioning of the optodes. There was little discomfort tothe volunteers due to point compression of the breast by the sourceoptode.

FIG. 3 shows an example of a typical set of measurements comprised ofeight spectra from the volunteer. Spectra from the respective bilateralpositions of the breast show symmetry, as expected in women with healthybreast tissue. In previous studies, this criterion of spectral symmetryin the same position of both breasts was used, by Egan and Dolan, toindicate absence of breast cancer in the examined areas (8). Thereproducibility of the optical transillumination measurements within onevisit was analyzed by recruiting a volunteer to undergo multipleprocedures.

A total of 8 spectra were collected per volunteer representing the fourquadrants (medial, distal, lateral and central) of both breasts. Thesespectra can be further pre-processed by correcting for tissue thickness(OD/cm) and/or auto-scaling prior to development and testing of themathematical models used to then correlate with tissue density. Ingeneral for Principal Component Analysis and similar models, scaling isused if the input variables have different units or if there are largedeviations in some input variables compared to other variables withinthe data set.

For auto-scaling, the data was mean-centered, where the mean opticaldensity value of the data set at each wavelength was subtracted from allspectra, and scaled to one unit variance by dividing all the values ateach wavelength by the standard deviation for that wavelength. This wasapplied only to the training set data for PCA models developed, whereasscaling of the validation set the previous obtained mean and standarddeviation vectors were used. See Table 3 for pre-processing options usedfor the interpretation of the spectral data set.

TABLE 3 Pre-processing techniques of the spectral dataset and areference number used in future sections. Pre-processing of DatasetTransfer function corrected I Thickness and transfer function correctedII Autoscaled and transfer function corrected III Autoscaled andthickness and transfer function corrected IV

A detailed example of establishing a correlation between optical spectraand disease risk follows for the example of PCA. It is generally foundthat data can be described in fewer components p_(i) than originalvariables (m=436 wavelength elements) and that the components can becombinations of variables that are useful descriptions of particularconstituents in the tissue. As stated above, the shape of the usefulcomponents will be a combination of spectral signatures of chromophoresthat vary with tissue density. Scores (elements of t_(i)) thatdifferentiate between tissue densities identify useful components(p_(i)). The scores (elements of t_(i)) can be graphically plottedagainst one another to show any potential clustering of spectra that arerelated. Here the PCA algorithm was trained on a test set (n=544spectra) and the same mathematical model, i.e. retaining the p_(i), wasused to predict the scores t_(i) on the validation set (n=192 spectra).

Statistical significance for the PCA prediction was established usinghigh density measure (HDM) as it is preferable over increased lowdensity measure (LDM) both are similar to sensitivity and specificity.As transillumination is envisioned as a method for identifying womenwith high tissue density within the entire population, improved HDM isdesired.

Consequently, HDM was used to assess the quality of the prediction modeldefined as the ratio of women predicted to have high density tissue tothose who were categorized as having high tissue density by theradiologist. LDM is a measure of the ability to correctly identify thosespectra that do not have high density.

FIG. 4 shows the correlation of the t₁ and t₂ scores from the spectrathat were repeatedly measured. FIG. 4 also shows acceptablereproducibility with slopes of the regression and the R² values close toone. There is a spread in the component scores (t₁ and t₂), but there isa clustering of the position related data.

FIG. 5 shows the principal components (p_(i)) from the PCA using n=544spectra (thickness and transfer function corrected) obtained to date.These first four components contain 97.6%, 1.2%, 0.6% and 0.3% of thevariance in the total data set, respectively, for a combined total of99.8% of the variance. The cluster plot of the scores for t₁ and t₂ isshown in FIG. 6. This plot illustrates discrimination of the breasttissue areas across a diagonal line in the t₁ vs. t₂ space.

Non-thickness corrected spectra were used to determine the effect ofthickness on the robustness of the PCA model prediction. The componentspectra for p₁-p₄ are very similar to the thickness-corrected componentplot; see FIGS. 5 and 7 respectively. The t₁ vs. t₂ plot, FIG. 8, forthis data shows discrimination as a function of t₂ only. Similarlycomponent spectra can be seen in FIG. 9 for thickness and transferfunction corrected data that were additionally autoscaled, with the t₁vs. t₂ cluster plot shown in FIG. 10. The scores for both t₁ and t₂ arecentered on zero as expected but show similar clusters as FIG. 8.

HDM and LDM were determined to compare which spectral pre-processingoption best differentiates the high and low density spectra in clusterplots according to FIGS. 6, 8 and 10. HDM and LDM data shown in theTable 4 were calculated from the same training and validation data setsseparately.

TABLE 4 HDM and LDM of Principal Component Analysis results for test andvalidation set measurements. Pre-processing Test set Validation Set ofDataset HDM LDM HDM LDM Transfer function I 85% 97.0%   88% 90%corrected (FIG. 8) Thickness and II 88% 93% 93% 89% transfer functioncorrected (FIG. 6) Autoscaled - III 86% 94% 90% 86% transfer functioncorrected (data not shown) Autoscaled - IV 87% 92% 93% 90% thickness andtransfer function corrected (FIG. 10)

Autoscaling removes some spectral information since the mean spectrum iswavelength dependent. As the spectral features contributing to thediscrimination between high and low breast density or risk are unknown,losing spectral information is very unwise. This is reflected in areduced HDM and LDM as seen in Table 4 for pre-processing option III andIV.

The scores t₁ and t₂ resulting from thickness corrected andnon-corrected component spectra demonstrated that it is possible todifferentiate between the subjects having low or high breast tissuedensity subjects (FIGS. 8 and 10).

While mathematical models derived for thickness or non-thicknesscorrected optical spectra differentiate high and low breast tissuedensities, their t₁ vs. t₂ cluster plots differ. One explanation for thedifference in the thickness corrected vs. non-corrected cluster plots isthe effect of the physical tissue thickness on the overall variancewithin the spectral data set and therefore the determination of thecomponents by the PCA algorithm. Thickness of the tissue contributesnon-uniformly to individual spectra as larger breasts tend to containmore fatty tissue.

In the model of the data that was not thickness corrected, p₁ did notdifferentiate between high and low density tissue. This can also be seenin the magnitude of the t₁ in FIG. 5 versus the non-thickness correctedcomponent spectra in FIG. 7. This indicates that the thickness valuescontribute to the magnitude of p₁ masking other contributions that dodifferentiate between tissue densities, such as light scattering. Thisleaves t₂ as the only component to preserve information to distinguishthe density of the breast.

When comparing the autoscaled versus non-autoscaled data, there wereminimal changes in the principal component spectra and minor differencesin HDM and LDM values, see FIGS. 6 and 10 and Table 4. Autoscaling aspart of the pre-processing can degrade regions with flat or extremespectral variation. (14) In this case, degraded spectral features couldinclude regions of the spectrum with minimal wavelength dependence andhence a first derivative close to zero. For example, the haemoglobininflection points are more pronounced in the non-autoscaled data than inthe autoscaled components. Conversely, the large spectral features ofwater and lipids are large compared to other structures in the spectra,but are less pronounced after autoscaling. In this study, the onlydifferences in the HDM values are in the training set with thenon-autoscaled data having almost 2% higher scores for both HDM and LDM.

Principal components can reveal particular regions of the spectrum thatrepresent important physical properties or entities within the tissuethat contribute to differentiation. Component spectra 1 (p₁) and 2 (p₂)are the most important cover the highest amount of variance in the dataset. While components 3 and 4 have similar or inverse shape as component2 they take less variance into account.

Principal component 1 (p₁) has a small dependence on wavelength and hasnegative values in each prepared model. The spectrally flatcharacteristics resulting from thickness corrected spectra can beattributed to attenuation due to absorption, scattering, and, thereforeoptical pathlength and losses at the tissue boundary. The surprisinglyflat spectral shape of the scattering contribution is due to thederivation of OD used here, based on the wavelength dependent transferfunction calibration by a polyurethane block which exhibits also highMie scattering, e.g. Mie scattering is present in the spectrum and hencecancels itself out. We propose that p₁ carries optical pathlengthinformation despite not showing the typical λ⁻¹ dependency, (18) butcontains information to determine breast density through the overallscattering power. Low density tissue spectra have a reduced amount ofscattering compared to high density tissue, and, therefore, highervalues of t₁ in FIGS. 8 and 10. This relationship in scatteringproperties is also seen in the scattering coefficient data by Peters etal. (19) and Troy et al. (20) supporting this interpretation of thefirst principal component.

The other component enabling differentiation between low and high tissuedensities p2, has a more complex shape when compared to p1. The mostimportant spectral features in the component are the lipid with inversewater peaks present at 930 nm and 980 nm, respectively. When t2 valuesare positive, the lipid peak is the dominant spectral feature asanticipated for fatty or low density tissue. Spectra from the highdensity tissue have negative t2 values and water absorption becomes thedominant structure in the component spectrum. Graham et al. (21) alsoobserved this relationship between water and density values when usingMRI to quantify percent density. In their study the water content of thetissue was measured directly and showed good correlation to percenttissue density (r=0.79). (21) Spectral feature contributions byhaemoglobin can be seen between 625 and 850 nm within p₂ where thenegative slope and inflection points of the haemoglobin curve areapparent. Dense breast tissue has large negative scores (t₂) compared tothe low density tissue. Haemoglobin contributes to the variance in p₂,with elevated contribution of blood related absorption for high waterabsorption seen in the higher density tissue. This simultaneousappearance of water and haemoglobin absorption can be explainedphysiologically, as tissues with higher water content and hence cellularcontent, require improved vascular supply and, thereby, increased bloodvolume. (22) Since positive t₂ scores are related to low tissue densityand positive t₁ scores are related to low tissue scatter, the clusterplot of t1 and t2 can be divided into quadrants as shown in FIG. 11,highlighting the relationship between the spectral features and theknown physical attributes of the breast tissue.

Even though p3 and p4 did not show differentiation between high and lowdensity tissue by themselves in a 2-dimensional plot like p1 and p2,areas of the component spectra can be interpreted. Additionally p3 showsa lipid absorption peak, but water and hemoglobin absorption are almostabsent additionally the lipid peak is shifted towards longer wavelength,so the reason therefore is unknown. p4 shows influence from thehemoglobin, with the same slope but inverse inflection points to p2.Even though differences in the amplitude and general shape of the curvesare minimal when compared to p2 the magnitude of the scores t3 and t4are much smaller than that of the first two components. One cannotexclude specifically p4 as a third contributor to increase HDM and LDMwhen using a three dimensional analysis as suggested in FIG. 12.

FIG. 13 shows an example of representing the parenchymal tissue densityon an interval scale (% dense area in the mammogram) rather than anominal scale (low, medium, high density). The % dense area can bedetermined using a computer assisted imaging program by a trainedobserver such as for example a radiologist. In the case presented hereno trained person was utilized and hence the percent density from themammograms contained a large error in the repeat measure whichultimately limits the accuracy of the presented correlation. However, itserves to indicate that the variance reduction programs employed arecapable of providing a single integer number to the physician monitoringhis patients' health.

The results disclosed herein show that In vivo optical transilluminationspectroscopy is a technically feasible method and capable of predictingbreast tissue densities with acceptable correlation to mammographicdensities as an indirect method of cancer risk assessment. Thus,transillumination spectroscopy may offer a novel “first step” in therisk assessment of healthy women regardless of menstrual cycle, age,ethnic background or menopausal status as the data here was notstratified by either event.

HDM and LDM values close to or above 90% are encouraging to distinguishbetween low and high density tissues. These HDM and LDM values arehigher compared with other physical examinations, such as ultrasound(23) and magnetic resonance imaging, (21) reported to be between 70-80%.

Optical transillumination spectroscopy offers the potential of areal-time and cost-effective method compared with ultrasound and has theability to quantify a large range of tissue densities for breasts thatare up to 6 cm in thickness. An added advantage of transilluminationspectroscopy over ultrasound and MRI is the fact that results arederived from preset mathematical models and, hence, no additional highlytrained personnel are required for assessment. This reduces the overallcost to the healthcare system for this risk-assessment technique. Apainless procedure and the inherent safety of this method will likelycontribute to a high compliance rate.

X-ray mammography uses ionizing radiation and is considered unacceptableas a tool to assess breast density for women less than forty years ofage and for frequent measurement. However, transilluminationspectroscopy is safe for women of all ages. This allows risk assessmentto commence at a much younger age when the life style and diet areperhaps easier to influence have more time to exert their beneficialeffects to reduce the risk and could ultimately lead to reducedincidence rates.

While optical transillumination spectroscopy is a promising tool tomonitor the effectiveness of chemopreventive, dietary or lifestylestudies for the reduction of breast cancer risk, its ability to detectphysical changes over a period of time in the breast tissue of a givenindividual needs to be demonstrated in a prospective longitudinal study.

The predictive value of optical spectroscopy for disease susceptibilityquantification can be increased through additional measurements whichcan include extending the optical waveband, obtain optical informationof interrogated tissue not contributing to the disease risk andobtaining information separately for light scattering.

The first option includes the use of wavelength up to 1.7 μm whichcontain among others additional water and lipid absorption bands usingan InGaAs based opto-electronic detector or an equivalent system. Thisis particular of interest for large tissue volumes. If the opticallyinterrogated tissue volume is small, that is in the range of severalmm³, the short wavelength band using light of approximately 360 nm to600 nm may provide the relevant information.

For the second option, using a combined transmittance and reflectancemeasurement as shown in FIG. 14 enables collection of the opticalinformation related to the superficial tissue, here skin, separatelythrough the reflectance technique, whereas the transilluminationspectroscopy contain information about the superficial and deep tissue.

As described above frequency or time domain reflectance andtransillumination measurements can provide an effective optical pathlength through phase shift or lifetime measurements. The optical pathlength relative to the physical path length between the optodes isrelated to the light scattering power of the tissue. FIG. 15 shows anexample of an embodiment of a system comprised of three diode lasers tomeasure the breast tissue light scattering power. The remaining part ofthe spectrum can be interpolated as shown in the paper by Cerussi et al(24).

The present method and apparatus disclosed herein has been exemplifiedusing breast cancer as the disease of interest which involvescorrelating the associated scalar coefficient of the basis spectralcomponent(s) with the pre-selected property of parenchymal breast tissuedensity known to be indicative of susceptibility of breast tissue forbreast cancer.

It will however be understood that this invention is not restricted touse in assessing risk for breast cancer but many other diseases as well.For example, the method disclosed herein is contemplated to beapplicable for correlating optical information of other mammalian tissuewith risk factors associated with diseases such as neurodegenerativediseases including Multiple Sclerosis, Alzheimer's and Parkihson'sdiseases; oncology including, prostate, rectal and testicular cancers,autoimmune diseases including Sinustisus, rheumatoid arthritis, andChron's disease. In each case the relevant tissues are optically sampledand the basis spectral component(s) are obtained using other techniquessuch as ultrasound, X-ray analysis, magnetic resonance imaging,potential molecular markers indicating initial changes in the tissue, orepidermologically derived questionnaires proven to correlate with thedisease of interest as just a couple of examples. The present inventionis applicable to mammals in general and is not restricted to humans.

As used herein, the terms “comprises”, “comprising”, “includes” and“including” are to be construed as being inclusive and open ended, andnot exclusive. Specifically, when used in this specification includingclaims, the terms “comprises”, “comprising”, “includes” and “including”and variations thereof mean the specified features, steps or componentsare included. These terms are not to be interpreted to exclude thepresence of other features, steps or components.

The foregoing description of the preferred embodiments of the inventionhas been presented to illustrate the principles of the invention and notto limit the invention to the particular embodiment illustrated. It isintended that the scope of the invention be defined by all of theembodiments encompassed within the following claims and theirequivalents.

REFERENCES

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1. A method for assessing susceptibility for developing a pre-selecteddisease in a mammal, comprising: a) illuminating a volume of selectedtissue of a mammal with light having wavelengths covering a pre-selectedspectral range; b) detecting light transmitted through, or reflectedfrom, said volume of selected tissue, and obtaining a spectrum of saiddetected light; c) representing the spectrum of detected light by a setof basis spectral components, an error term, and an associated scalarcoefficient for each basis spectral component in said set, the set ofbasis spectral components including at least one basis spectralcomponent, the associated scalar coefficient for each basis spectralcomponent being calculated by minimizing the error term; and d)correlating the associated scalar coefficient for each basis spectralcomponent with a pre-selected property of the selected tissue known tobe indicative of susceptibility of the tissue for the pre-selecteddisease to obtain the susceptibility for the mammal to developing thepre-selected disease: wherein the set of basis spectral components ofstep (c) is obtained by the steps of: obtaining a range of values of thepre-selected property from a population cross section which isrepresentative of the mammalian population at large, but who are notexpressing the pre-selected disease; in each mammalian member of saidpopulation cross section obtaining at least one spectrum by illuminatinga volume of selected tissue with light having wavelengths covering thepre-selected spectral range, and detecting light transmitted through, orreflected from, said volume of selected tissue; extracting commonspectral elements from the at least one spectrum of each member of saidpopulation cross section and producing at least one set of componentspectra from the extracted common spectral elements, the at least oneset of component spectra including at least one component spectra; andidentifying a set of component spectra which gives the highestpredictive value for the pre-selected property of the selected tissueknown to be indicative of susceptibility for the pre-selected disease;wherein said set of component spectra which gives the highest predictivevalue for the pre-selected property of the selected tissue known to beindicative of susceptibility for the pre-selected disease is used as theset of basis spectral components in step c).
 2. The method according toclaim 1 wherein the step of extracting common spectral elements from theat least one spectrum of each member of said population cross sectionand producing at least one set of component spectra from the extractedcommon spectral elements includes using one of Principle ComponentAnalysis, Partial Least Squares and Principle Component Regression. 3.The method according to claim 1 wherein the step of calculating theassociated scalar coefficient by minimizing the error term includesusing Chi-square fitting to minimize the error term.
 4. The methodaccording to claim 1 including repeating steps a) to d) inclusively fora particular mammal periodically over a pre-selected period of themammal's lifetime and storing the susceptibilities calculatedperiodically to detect a rate of change in susceptibility for increasedrisk for the pre-selected disease.
 5. The method according to claim 1wherein said mammal is a post puberty human female, and wherein thepre-selected disease is breast cancer, and the pre-selected property isparenchymal breast tissue density.
 6. The method according to claim 5wherein the parenchymal breast tissue density is measured by one ofx-ray mammography, ultrasound, computed tomography, cone beam computedtomography, electrical impedance spectroscopy and magnetic resonanceimaging.
 7. The method according to claim 1 wherein the spectrum of saiddetected light is a continuous spectrum.
 8. The method according toclaim 1 wherein the spectrum of said detected light is a discretespectrum.
 9. The method according to claim 1 wherein the step ofilluminating a volume of selected tissue of a mammal with light havingwavelengths covering a pre-selected spectral range includes illuminatingwith broad band light, discrete wavelengths or wavelength bands.
 10. Themethod according to claim 1 wherein the steps of illuminating a volumeof selected tissue and detecting light transmitted through, or reflectedfrom, said volume of selected tissue includes adjusting an angle and adistance between a position on the tissue surface which is illuminatedand a position on the tissue surface where the transmitted or reflectedlight emanates from which is detected.
 11. The method according to claim1 wherein said pre-selected disease is selected from the groupconsisting of neurodegenerative diseases, oncology based diseases andautoimmune diseases.
 12. The method according to claim 11 wherein saidneurodegenerative diseases include Multiple Sclerosis, Alzheimer's andParkinson's disease, and wherein said oncology based diseases includeprostate, rectal and testicular cancers, and wherein said autoimmunediseases include sinustisus, rheumatoid arthritis, and Crohn's disease.13. The method according to claim 1 wherein the set of basis spectralcomponents includes two or more component spectra.
 14. The methodaccording to claim 1 wherein step d) includes using an effectivemathematical variance reduction model to identify the basis spectralcomponents which through the associated scalar coefficient shows bestcorrelation with the pre-selected property of the selected tissue knownto be indicative of susceptibility of the tissue for the pre-selecteddisease.
 15. An apparatus for optical assessing susceptibility fordeveloping a pre-selected disease in a mammal, comprising: a) holdermeans for holding and immobilizing an anatomical part of a mammalcontaining tissue to be optically probed; b) light source means forilluminating a volume of selected tissue of a mammal with light havingwavelengths covering a pre-selected spectral range; c) detection meansfor detecting light transmitted through, or reflected from, said volumeof selected tissue; d) computer control means connected to saiddetection means for producing a spectrum of said detected light from anoutput of said detection means, the computer control means includingprocessing means for representing the spectrum of detected light by aset of basis spectral components, an error term, and an associatedscalar coefficient for each spectral component in said set, the set ofbasis spectral components including at least one basis spectralcomponent, the associated scalar coefficient for each basis spectralcomponent being calculated by minimizing the error term, the processingmeans includes means for correlating the associated scalar coefficientfor each basis spectral component with a pre-selected property of theselected tissue known to be indicative of susceptibility of the tissuefor the pre-selected disease to obtain the susceptibility for the mammalto developing the pre-selected disease, the computer control meansincluding display means for displaying the susceptibility.
 16. Theapparatus according to claim 15 wherein the at least one basis spectralcomponent is obtained by the steps of obtaining a range of values of thepre-selected property from a population cross section which isrepresentative of the mammalian population at large, but who are notexpressing the pre-selected disease; in each mammalian member of saidpopulation cross section obtaining at least one spectrum by illuminatinga volume of selected tissue with light having wavelengths covering thepre-selected spectral range, and detecting light transmitted through, orreflected from, said volume of selected tissue; extracting commonspectral elements from the at least one spectrum of each member of saidpopulation cross section and producing at least one set of componentspectra from the extracted common spectral elements, the at least oneset of component spectra including at least one component spectra;identifying a set of component spectra which gives the highestpredictive value for the pre-selected property of the selected tissueknown to be indicative of susceptibility for the pre-selected disease;and using said set of component spectra which gives the highestpredictive value for the pre-selected property of the selected tissueknown to be indicative of susceptibility for the pre-selected disease asthe set of basis spectral components in claim
 15. 17. The apparatusaccording to claim 15 wherein the light source means includes either oneof a white light source such as a filtered halogen lamp, a series ofbandwidth limited light emitting diodes (LEDs), several laser lightsources all emitting within the pre-selected spectral range.
 18. Theapparatus according to claim 15 wherein the detection means includeseither wavelength selective means in conjunction with a charge coupleddevice or a photodiode array all for parallel detection of thepre-selected wavelength band or an avalanche photodiode or point photodetector for sequential detection of the pre-selected wavelength band.19. The apparatus according to claim 15 wherein the processing meansincludes calibration means for measuring a wavelength dependentinstrument transfer function, and wherein said processing means includesmeans for removing instrumental affects on the spectrum of detectedlight.
 20. The apparatus according to claim 18 wherein the calibrationmeans for measuring a wavelength dependent instrument transfer functionincludes a pre-selected volume of material which preferentially scatterslight rather than absorbing light, positioned in place of thepre-selected tissue.
 21. The apparatus according to claim 20 wherein thematerial which preferentially scatters light rather than absorbing lightis comprised of highly density poly-urethane.
 22. The apparatusaccording to claim 15 wherein said detection means is positioned todetect light reflected from the tissue being illuminated, includingmeans for adjusting the angle and distance between the light sourcemeans and the detection means.
 23. The apparatus according to claim 15wherein the light source means includes a light source and an opticalfiber with a proximal end positioned to couple light into said opticalfiber and a distal end from which light emanates from the fiberpositioned so as to direct light onto the pre-selected tissue.
 24. Theapparatus according to claim 15 wherein the mammal is a post pubertyhuman female, and wherein the pre-selected disease is breast cancer, andwherein the holder means includes a breast support including at leastone illumination means and at least one detection means for each breast.